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December 22, 2025

今年の展望:2026年に注目すべきAIサイバーセキュリティのトレンド

毎年、ダークトレースのエキスパート達は、日々発生するインシデント、脆弱性、ニュースの動きを客観的に振り返り、脅威ランドスケープを形作るさまざまな力について考察することにより、これからの1年で最も重要になると思われるトレンドを調べ、発表しています。2026年に対する私たちの予測は次の通りです。
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Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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22
Dec 2025

はじめに:2026年のサイバー脅威トレンド

毎年、私たちは社内のエキスパートに聞き取り調査を実施し、日々発生するインシデント、脆弱性、ニュースの動きを客観的に振り返り、脅威ランドスケープを形作るさまざまな力について考察しています。目的はシンプルです。それは、顧客が直面している現実の課題、R&Dチームが研究している技術や問題、そして攻撃者と防御者の双方がどのように適応しているかに基づいて、今後1年間で最も重要となると思われるトレンドを特定し、共有することです。

2025年、生成AIおよび初期のエージェント型システムが、限られたパイロットプロジェクトでの運用からより広範な採用へと拡大していきました。生成AIツールが、日常的に使用されるSaaS製品や企業のワークフローに埋め込まれ、AIエージェントがより多くのデータやシステムにアクセスするようになり、私たちは脅威アクターがどのように商用AIモデルを操作し攻撃に使用するのか、その片鱗を確認しました。同時に、拡大するクラウドおよびSaaSエコシステム、そして自動化の使用の増加により、従来のセキュリティの前提にはますます無理が生じています。

2026年を展望するにあたり、AIモデル、エージェント、そしてそれらを動かすアイデンティティが、攻撃者と防御者の両方にとって、緊張 – と同時に機会 – のキーポイントとなりつつあることがすでに見て取れます。アイデンティティ、信頼、データ完全性、人間による意思決定など、長期的な課題およびリスクがなくならない一方で、AIと自動化によりサイバーリスクのスピードと規模は拡大するでしょう。

以下は当社のエキスパートが確信する、サイバーセキュリティの次のフェーズを形成するであろうトレンド、および組織が備えるべき現実です。

次の重大内部関係者リスクはエージェント型AI

2026年、さまざまな組織がエージェント型AIの意図しない挙動による初の大規模なセキュリティインシデントを経験するでしょう。これらは必ずしも悪意によるものとは限りませんが、エージェントが如何に簡単に影響を受けてしまうかということに起因します。AIエージェントはその設計上、人を助けますが、思慮に欠け、前後関係や影響を理解せずに動作します。そのため非常に効率的であると同時に、非常に影響されやすいとも言えます。人間の内部関係者とは異なり、エージェント型システムはソーシャルエンジニアリングで操られたり、脅迫されたり、買収されたりする必要がありません。クリエイティブなプロンプトを入力される、正しいプロンプトを間違って解釈する、あるいは間接的なプロンプトインジェクションに脆弱であるだけでよいのです。アクセス、範囲、振る舞いについての強力なコントロールが存在しなければ、エージェントはデータを不必要に共有したり、コミュニケーションの転送先を間違えたり、重大なビジネスリスクを招くアクションを実行してしまったりする可能性があります。AIの導入を安全に行うためには、エージェントを最高レベルのアイデンティティとして扱い、意図に基づいてではなくその振る舞いに基づいて監視し、制約し、評価する必要があります。

-- ニコール・キャリナン(Nicole Carignan)、セキュリティおよびAI戦略担当上級副社長

プロンプトインジェクションは理論段階からトップニュースとなるような侵害の発生へ

2026年、AIを導入した企業に対する間接的なプロンプトインジェクション攻撃についての初めての大きなニュースを目にすることになるでしょう。アクセスしやすいチャットボットあるいはエージェント型システムが隠されたプロンプトを取り込むことによる侵害です。実際問題として、AIシステムによる承認されないデータ露出や意図しない有害な振る舞い、たとえば不必要な情報の共有、コミュニケーションの転送間違い、あるいは意図した範囲を超えたアクションなどが発生するでしょう。このリスクが最近注目されていることは -特にAIを使用したブラウザおよび追加的セーフティレイヤーによりエージェントの動作をガイドするという文脈において-この課題に対する業界の認識の高まりを示しています。

-- コリン・シャプロウ(Collin Chapleau)、セキュリティ& AI戦略担当シニアディレクター

人間はますますついていけない状況に

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The サイバーに関しては、人間が失敗しているのではありません。システムが人間にはついていけない速度で動作しているのです。攻撃者は人間の判断力とマシンスピードで実行されるオペレーションの隙間を悪用しているのです。過去数年に見られるディープフェイクや感情に訴える詐欺の増加は、私たちが注意するようにこれまで教えられてきた、人間的な手掛かりに気づく能力を超えています。詐欺は今やソーシャルプラットフォームや暗号化されたチャットに拡大しており、数分で支払いまで終了します。人間に対して最終防衛線としての期待をすることは現実的ではありません。

防御は人間の間違いやすさを前提として設計されなければなりません。自動化された出処チェック、暗号署名、デュアルチャネル検証などを人間の判断の前に行うべきです。トレーニングは重要ではありますが、それだけでは隙間を埋めることはできません。これからの1年、パートナーシップにより注目すべきです。それはシステムがリスクを吸収し、人間がプレッシャーを受けてではなくコンテキストに基づいた判断が可能になる関係です。

-- マーガレット・カニンガム(Margaret Cunningham)、セキュリティ & AI戦略担当副社長

AIは攻撃者のボトルネックを解消 -より小規模な組織が影響を受ける

現在、多くの企業で侵害が発生していない要因の1つは攻撃者側のボトルネックです。人間のハッカー資源が足りないということです。キーボードを操る人間の数は脅威ランドスケープにおいて速度を左右する条件の1つです。AIと自動化技術の進化によりこのボトルネックがますます解消されていくでしょう。すでにこの傾向は確認されています。自社は目立たなすぎて攻撃者に気づかれないことを願う「ダチョウ型」アプローチは攻撃者のキャパシティが拡大する中でもはや機能しなくなるでしょう。

-- マックス・ハイネメイヤー(Max Heinemeyer)、グローバルフィールドCISO

SaaSプラットフォームが格好のサプライチェーン標的に

攻撃者は簡単なことを学びました。それは、SaaSプラットフォームを侵害すると大きな利益につながる場合があるということです。その結果、高い信頼を受けビジネス環境に深く組み込まれている、一般的な商用SaaSプロバイダーが標的となることが増えています。こうした攻撃の一部は、あまりなじみのないブランドのソフトウェアが関係したものかもしれませんが、それらが下流に及ぼす影響は非常に大きくなります。2026年には、攻撃者が正規の認証情報、API、あるいは設定ミスを利用して従来の防御を完全に回避するような侵害が増えると予想されます。

-- ナサニエル・ジョーンズ(Nathaniel Jones)、セキュリティ & AI戦略担当副社長

サイバー攻撃用生成AIおよびAIアシスタントの商業化が進む

2026年、私たちが注目しているトレンドの1つは、AI支援によるサイバー犯罪の商業化です。たとえば、サイバー犯罪用プロンプトプレイブックがダークウェブ上で販売されています。これは簡単に言えば攻撃者にAIモデルの不正使用またはジェイルブレイクの方法を示す、コピー&ペーストで使えるフレームワークです。これはAIがサイバー犯罪への参入障壁を引き下げるという、2025年に見られた傾向がさらに進んだものです。2026年には、これらのテクニックが製品化され、スケール可能となり、再利用も格段に簡単になることが予想されます。

-- トビー・ルイス(Toby Lewis)、脅威分析グローバルヘッド

結論

これらのトレンドを合わせて考えると、サイバーセキュリティの中核的課題、たとえばアイデンティティ、信頼、データ、人間の判断、これらは劇的に変化しているわけではなく、依然としてほとんどのインシデントの根本に存在します。急激に変化しているのは、これらの課題が現れる環境です。AIと自動化が、攻撃者のスケール速度、リスクが拡大する規模、そして意図しない動作がいかに簡単に重大な事態を招く結果となるかということを含めすべてを加速しています。そして、クラウドサービスやSaaSプラットフォーム等のテクノロジーがさらに深くビジネスに組み込まれるのと同時に、潜在的アタックサーフェスも拡大を続けています。  

予測が現実になる保証はありません。しかし現在出現しつつあるパターンが示していることは、2026年が、AIを保護することがビジネス全体を保護することと切り離せなくなる年になるだろうということです。AIがどのように使用され、どのように振る舞い、そのように不正使用され得るかを理解することにより、このことに今から備える組織は、今後1年間にこれらのテクノロジーを自信を持って導入できる可能性が高いでしょう。

組織のAI導入を安全に、侵害を招くことなく実現する方法についてさら詳しく知るには、2026年2月3日に開催されるダークトレースのライブウェビナーにご参加ください。

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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The Darktrace Community

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May 12, 2026

Resilience at the Speed of AI: Defending the Modern Campus with Darktrace

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Why higher education is a different cybersecurity battlefield

After four decades in IT, now serving as both CIO and CISO, I’ve learned one simple truth: cybersecurity is never “done.” It’s a constant game of cat and mouse. Criminals evolve. Technologies advance. Regulations expand. But in higher education, the challenge is uniquely complex.

Unlike a bank or a military installation, we can’t lock down networks to a narrow set of approved applications. Higher education environments are open by design. Students collaborate globally, faculty conduct cutting-edge research, and administrators manage critical operations, all of which require seamless access to the internet, global networks, cloud platforms, and connected systems.

Combine that openness with expanding regulatory mandates and tight budgets, and the balancing act becomes clear.

Threat actors don’t operate under the same constraints. Often well-funded and sponsored by nation-states with significant resources, they’re increasingly organized, strategic, and innovative.

That sophistication shows up in the tactics we face every day, from social engineering and ransomware to AI-driven impersonation attacks. We’re dealing with massive volumes of data, countless signals, and a very small window between detection and damage.

No human team, no matter how talented or how numerous, can manually sift through that noise at the speed required.

Discovering a force multiplier

Nothing in cybersecurity is 100% foolproof. I never “set it and forget it.” But for institutions balancing rising threats and finite resources, the Darktrace ActiveAI Security Platform™ offers something incredibly valuable: peace of mind through speed and scale.

It closes the gap between detection and response in a way humans can’t possibly match. At the speed of light, it can quarantine, investigate, and contain anomalous activity.

I’ve purchased and deployed Darktrace three separate times at three different institutions because I’ve seen firsthand what it can do and what it enables teams like mine to achieve.

I first encountered Darktrace while serving as CIO for a large multi-campus college system. What caught my attention was Darktrace's Self-Learning AI, and its ability to learn what "normal" looked like across our network. Instead of relying solely on static signatures or rigid rules, Darktrace built a behavioral baseline unique to our environment and alerted us in real time when something simply didn’t look right.

In higher education, where strict lockdowns aren’t realistic, that behavioral model made all the difference. We deployed it across five campuses, and the impact was immediate. Operating 24/7, Darktrace surfaced threats in ways our team couldn’t replicate manually.

Over time, the Darktrace platform evolved alongside the changing threat landscape, expanding into intrusion prevention, cloud visibility, and email security. At subsequent institutions, including Washington College, Darktrace was one of my first strategic investments.

Revealing the hidden threat other tools missed

One of the most surprising investigations of my career involved a data leak. Leadership suspected sensitive information from high-level meetings was being exposed, but our traditional tools couldn’t provide any answers.

Using Darktrace’s deep network visibility, down to packet-level data, we traced unusual connections to our CCTV camera system, which had been configured with a manufacturer’s default password. A small group of employees had hacked into the CCTV cameras, accessed audio-enabled recordings from boardroom meetings, and stored copies locally.

No other tool in our environment could have surfaced those connections the way Darktrace did. It was a clear example of why using AI to deeply understand how your organization, systems, and tools normally behave, matters: threats and risks don’t always look the way we expect.

Elevating a D-rating into a A-level security program

When I arrived at my last CISO role, the institution had recently experienced a significant ransomware attack. Attackers located  data  which informed their setting  ransom demands to an amount they knew would likely result in payment. It was a sobering example of how calculated and strategic modern cybercriminals have become.

Third-party cyber ratings reflected that reality, with a  D rating.

To raise the bar, we implemented a comprehensive security program and integrated layered defenses; -deploying state of the art tools and methods-  across the environment, with Darktrace at its core.

After a 90-day learning period to establish our behavioral baseline, we transitioned the platform into fully autonomous mode. In a single 30-day span, Darktrace conducted more than 2,500 investigations and autonomously resolved 92% of all false positives.

For a small team, that’s transformative. Instead of drowning in alerts, my staff focused on less than  200 meaningful cases that warranted human review.

Today, we maintain a perfect A rating from third-party assessors and have remained cybersafe.

Peace of mind isn’t about complacency

The effect of Darktrace as a force multiplier has a real human impact.

With the time reclaimed through automation, we expanded community education programs and implemented simulated phishing exercises. Through sustained training and awareness efforts, we reduced social engineering susceptibility from nearly 45% to under 5%.

On a personal level, Darktrace allows me to sleep better at night and take time off knowing we have intelligent systems monitoring and responding around the clock. For any CIO or CISO carrying institutional risk on their shoulders, that matters.

The next era: AI vs. AI

A new chapter in cybersecurity is unfolding as adversaries leverage AI to enhance scale, speed, and believability. Phishing campaigns are more personalized, impersonation attempts are more precise, and deepfake video technology, including live video, is disturbingly authentic. At the same time, organizations are rapidly adopting AI across their own environments —from GenAI assistants to embedded tools to autonomous agents. These systems don’t operate within fixed rules. They act across email, cloud, SaaS, and identity systems, often with broad permissions, and their behavior can evolve over time in ways that are difficult to predict or control.

That creates a new kind of security challenge. It’s not just about defending against AI-powered threats but understanding and governing how AI behaves within your environment, including what it can access, how it acts, and where risk begins to emerge.

From my perspective, this is a natural next step for Darktrace.

Darktrace brings a level of maturity and behavioral understanding uniquely suited to the complexity of AI environments. Self-Learning AI learns the normal patterns of each business to interpret context, uncover subtle intent, and detect meaningful deviations without relying on predefined rules or signatures. Extending into securing AI by bringing real-time visibility and control to GenAI assistants, AI agents, development environments and Shadow AI, feels like the logical evolution of what Darktrace already does so well.

Just as importantly, Darktrace is already built for dynamic, cross-domain environments where risk doesn’t sit in a single tool or control plane. In higher education, activity already spans multiple systems and, with AI, that interconnection only accelerates.

Having deployed Darktrace multiple times, I have confidence it’s uniquely positioned to lead in this space and help organizations adopt AI with greater visibility and control.

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Since authoring this blog, Irving Bruckstein has transitioned to the role of Chief Executive Officer of the Cyberaigroup.

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Irving Bruckstein
CEO CyberAIgroup

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May 11, 2026

The Next Step After Mythos: Defending in a World Where Compromise is Expected

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Is Anthropic’s Mythos a turning point for cybersecurity?

Anthropic’s recent announcements around their Mythos model, alongside the launch of Project Glasswing, have generated significant interest across the cybersecurity industry.

The closed-source nature of the Mythos model has understandably attracted a degree of skepticism around some of the claims being made. Additionally, Project Glasswing was initially positioned as a way for software vendors to accelerate the proactive discovery of vulnerabilities in their own code; however, much of the attention has focused on the potential for AI to identify exploitable vulnerabilities for those with malicious intent.

Putting questions around the veracity of those claims to one side – which, for what it’s worth, do appear to be at least partially endorsed by independent bodies such as the UK’s AI Security Institute – this should not be viewed as a critical turning point for the industry. Rather, it reflects the natural direction of travel.

How Mythos affects cybersecurity teams  

At Darktrace, extolling the virtues of AI within cybersecurity is understandably close to our hearts. However, taking a step back from the hype, we’d like to consider what developments like this mean for security teams.

Whether it’s Mythos or another model yet to be released, it’s worth remembering that there is no fundamental difference between an AI discovered vulnerability and one discovered by a human. The change is in the pace of discovery and, some may argue, the lower the barrier to entry.

In the hands of a software developer, this is unquestionably positive. Faster discovery enables earlier remediation and more proactive security. But in the hands of an attacker, the same capability will likely lead to a greater number of exploitable vulnerabilities being used in the wild and, critically, vulnerabilities that are not yet known to either the vendor or the end user.

That said, attackers have always been able to find exploitable vulnerabilities and use them undetected for extended periods of time. The use of AI does not fundamentally change this reality, but it does make the process faster and, unfortunately, more likely to occur at scale.

While tools such as Darktrace / Attack Surface Management and / Proactive Exposure Management  can help security teams prioritize where to patch, the emergence of AI-driven vulnerability discovery reinforces an important point: patching alone is not a sufficient control against modern cyber-attacks.

Rethinking defense for a world where compromise is expected

Rather than assuming vulnerabilities can simply be patched away, defenders are better served by working from the assumption that their software is already vulnerable - and always will be -and build their security strategy accordingly.

Under that assumption, defenders should expect initial access, particularly across internet exposed assets, to become easier for attackers. What matters then is how quickly that foothold is detected, contained, and prevented from expanding.

For defenders, this places renewed emphasis on a few core capabilities:

  • Secure-by-design architectures and blast radius reduction, particularly around identity, MFA, segmentation, and Zero Trust principles
  • Early, scalable detection and containment, favoring behavioral and context-driven signals over signatures alone
  • Operational resilience, with the expectation of more frequent early-stage incidents that must be managed without burning out teams

How Darktrace helps organizations proactively defend against cyber threats

At Darktrace, we support security teams across all three of these critical capabilities through a multi-layered AI approach. Our Self-Learning AI learns what’s normal for your organization, enabling real-time threat detection, behavioral prediction, incident investigation and autonomous response. - all while empowering your security team with visibility and control.

To learn more about Darktrace’s application of AI to cybersecurity download our White Paper here.  

Reducing blast radius through visibility and control

Secure-by-design principles depend on understanding how users, devices, and systems behave. By learning the normal patterns of identity and network activity, Darktrace helps teams identify when access is being misused or when activity begins to move beyond expected boundaries. This makes it possible to detect and contain lateral movement early, limiting how far an attacker can progress even after initial access.

Detecting and containing threats at the earliest stage  

As AI accelerates vulnerability discovery, defenders need to identify exploitation before it is formally recognized. Darktrace’s behavioral understanding approach enables detection of subtle deviations from normal activity, including those linked to previously unknown vulnerabilities.

A key example of this is our research on identifying cyber threats before public CVE disclosures, demonstrating that assessing activity against what is normal for a specific environment, rather than relying on predefined indicators of compromise, enables detection of intrusions exploiting previously unknown vulnerabilities days or even weeks before details become publicly available.

Additionally, our Autonomous Response capability provides fast, targeted containment focused on the most concerning events, while allowing normal business operations to continue. This has consistently shown that even when attackers use techniques never seen before, Darktrace’s Autonomous Response can contain threats before they have a chance to escalate.

Scaling response without increasing operational burden

As early-stage incidents become more frequent, the ability to investigate and respond efficiently becomes critical. Darktrace’s Cyber AI Analyst’s AI-driven investigation capabilities automatically correlate activity across the environment, prioritizing the most significant threats and reducing the need for manual triage. This allows security teams to respond faster and more consistently, without increasing workload or burnout.

What effective defense looks like in an AI-accelerated landscape

Developments like Mythos highlight a reality that has been building for some time: the window between exposure and exploitation is shrinking, and in many cases, it may disappear entirely. In that environment, relying on patching alone becomes increasingly reactive, leaving little room to respond once access has been established.

The more durable approach is to assume that compromise will occur and focus on controlling what happens next. That means identifying early signs of misuse, containing threats before they spread, and maintaining visibility across the environment so that isolated signals can be understood in context.

AI plays a role on both sides of this equation. While it enables attackers to move faster, it also gives defenders the ability to detect subtle changes in behavior, prioritize what matters, and respond in real time. The advantage will not come from adopting AI in isolation, but from applying it in a way that reduces the gap between detection and action.

AI may be accelerating parts of the attack lifecycle, but the fundamentals of defense, detection, and containment still apply. If anything, they matter more than ever – and AI is just as powerful a tool for defenders as it is for attackers.

To learn more about Darktrace and Mythos read more on our blog: Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

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Toby Lewis
Head of Threat Analysis
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